Location of Repository

Automatic positive semidefinate HAC covariance matrix and GMM estimation

By Richard J. Smith

Abstract

This paper proposes a new class of heteroskedastic and autocorrelation consistent (HAC) covariance matrix estimators. The standard HAC estimation method reweights estimators of the autocovariances. Here we initially smooth the data observations themselves using kernel function–based weights. The resultant HAC covariance matrix estimator is the normalized outer product of the smoothed random vectors and is therefore automatically positive semidefinite. A corresponding efficient GMM criterion may also be defined as a quadratic form in the smoothed moment indicators whose normalized minimand provides a test statistic for the overidentifying moment conditions

Topics: HB, QA
Publisher: Cambridge University Press
Year: 2005
OAI identifier: oai:wrap.warwick.ac.uk:733

Suggested articles

Preview

Citations

  1. b b0! 2 ( s1T T1 ( rmax@1,1s# min@T,Ts# grs~ N b!]gr~ N b!0]b'0T (A.1) ( t1r
  2. (1991). Heteroskedasticity and autocorrelation consistent covariance matrix estimation+ doi
  3. Therefore, ~T0ST 2! Z QT~ Z b!0~k1!2 ~T0~STk1!2! [ gT~b0!'P [ gT~b0!op~1!, where P[V 1 V1GSG'V1+ The second conclusion follows from Lemma A+2o fS m i t h~2001!, PVP P, and rk~P! m p+ 170 RICHARD J. SMITH
  4. w+p+a+1+ Therefore the first-order conditions Z GT~ Z b!' Z VT~ D b!1 [ gT~ Z b! 0w +p+a+1, where Z GT~b![][ gT~b!0 ]b'+ By the mean value theorem, [ gT~ Z b!

To submit an update or takedown request for this paper, please submit an Update/Correction/Removal Request.